Dysregulation of neutrophil biology, including an increase in circulating numbers, is a key characteristic of sepsis that has long provided diagnostic value. Recently, more detailed in vitro analyses have also identified specific changes in neutrophil function and behavior, including altered phagocytic activity and chemotaxis. However, traditional in vitro assays of neutrophil function only provide endpoint readouts and do not facilitate detailed measurement of complex migration phenotypes.
In this study, we designed a microfluidic device to assess complex behavioral phenotypes of neutrophils migrating spontaneously from a droplet of diluted blood. The device consisted of a simple maze that allowed measurement of multiple aspects of neutrophil migration, better mirroring what can be observed in vivo.
Application of machine-learning approaches to complex neutrophil migration datasets identified key behaviors that segregated with septic patients in a derivation cohort of at-risk patients. Integration of these characteristics into a scoring strategy allowed diagnostic and predictive identification of sepsis in patients with AUROC values greater than 0.95. Swapping of plasma between patients and healthy individuals demonstrated that spontaneous neutrophil motility is driven at least in part by factors in the plasma. The scoring system was tested on a second, independent validation cohort in a double-blinded study, and accurately identified patients with sepsis. These studies highlight the importance of neutrophils in sepsis, and identify changes in neutrophil behavior as a useful and highly accurate sepsis diagnostic.